Spatiotemporal traffic forecasting models become a popular tool of urban transport engineering. Performance of spatiotemporal models and their generalisation abilities are the key aspects that are intensively addressed in methodological literature and case studies. This paper proposes a spatiotemporal cross-validation approach to estimating model performance, which extends classical temporal cross-validation techniques to a complex spatiotemporal structure of traffic flow relationships. The proposed approach allows estimating model generalisation abilities in the spatiotemporal dimension – ability to forecast traffic flows at unobserved nearby road segments. Additionally, the spatiotemporal cross-validation provides clues for stability of model performance with respect to minor modifications of the spatial structure. Advantages of the proposed spatiotemporal cross-validation approach are demonstrated on a large citywide traffic data set.
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